package job_types

import (
	"context"
	"fmt"
	"time"

	"gitlab.ctyuncdn.cn/tai/infra/cloud-controller/internal/job_manager"
	"gitlab.ctyuncdn.cn/tai/infra/cloud-controller/pkg/mysql"
)

// TrainingJobTypeHandler handles training jobs
type TrainingJobTypeHandler struct {
	*BaseJobTypeHandler
}

// NewTrainingJobTypeHandler creates a new training job type handler
func NewTrainingJobTypeHandler() *TrainingJobTypeHandler {
	base := NewBaseJobTypeHandler(job_manager.JobTypeTraining, "Machine Learning Training job handler")

	// Add training-specific parameters
	trainingParams := []job_manager.Parameter{
		{
			Name:        "script_path",
			Type:        "string",
			Required:    true,
			Description: "Path to the training script",
			Validation:  "script_path != ''",
		},
		{
			Name:        "data_path",
			Type:        "string",
			Required:    true,
			Description: "Path to the training data",
			Validation:  "data_path != ''",
		},
		{
			Name:        "model_path",
			Type:        "string",
			Required:    true,
			Description: "Path to save the trained model",
			Validation:  "model_path != ''",
		},
		{
			Name:        "epochs",
			Type:        "int",
			Required:    false,
			Default:     100,
			Description: "Number of training epochs",
			Validation:  "epochs > 0",
		},
		{
			Name:        "batch_size",
			Type:        "int",
			Required:    false,
			Default:     32,
			Description: "Training batch size",
			Validation:  "batch_size > 0",
		},
		{
			Name:        "learning_rate",
			Type:        "float",
			Required:    false,
			Default:     0.001,
			Description: "Learning rate for training",
			Validation:  "learning_rate > 0",
		},
		{
			Name:        "framework",
			Type:        "string",
			Required:    false,
			Default:     "pytorch",
			Description: "Deep learning framework to use",
			Validation:  "framework in ['pytorch', 'tensorflow', 'keras']",
		},
		{
			Name:        "gpu_count",
			Type:        "int",
			Required:    false,
			Default:     0,
			Description: "Number of GPUs to use for training",
			Validation:  "gpu_count >= 0",
		},
	}

	// Combine base and training-specific parameters
	allParams := append(base.supportedParameters, trainingParams...)
	base.supportedParameters = allParams

	return &TrainingJobTypeHandler{
		BaseJobTypeHandler: base,
	}
}

// ValidateJob validates a training job
func (h *TrainingJobTypeHandler) ValidateJob(job *mysql.Job) error {
	// Call base validation
	if err := h.BaseJobTypeHandler.ValidateJob(job); err != nil {
		return err
	}

	// Additional training-specific validation
	if job.Parameters == "" {
		return fmt.Errorf("training job requires parameters")
	}

	// TODO: Parse and validate training-specific parameters
	// This would include checking for required script_path, data_path, model_path parameters

	return nil
}

// CreateResources creates resources for a training job
func (h *TrainingJobTypeHandler) CreateResources(ctx context.Context, job *mysql.Job) ([]*job_manager.Resource, error) {
	// Create training-specific resources
	resources := []*job_manager.Resource{
		{
			Type:      "Pod",
			Name:      fmt.Sprintf("training-%s-pod", job.JobID),
			Namespace: "training",
			Spec: map[string]interface{}{
				"restartPolicy": "Never",
				"containers": []map[string]interface{}{
					{
						"name":  "training",
						"image": "pytorch/pytorch:latest",
						"command": []string{
							"python",
							"{{.ScriptPath}}",
						},
						"args": []string{
							"--data-path", "{{.DataPath}}",
							"--model-path", "{{.ModelPath}}",
							"--epochs", "{{.Epochs}}",
							"--batch-size", "{{.BatchSize}}",
							"--learning-rate", "{{.LearningRate}}",
						},
						"env": []map[string]interface{}{
							{
								"name":  "PYTHONPATH",
								"value": "/workspace",
							},
							{
								"name":  "CUDA_VISIBLE_DEVICES",
								"value": "{{.GPUDevices}}",
							},
						},
						"resources": map[string]interface{}{
							"requests": map[string]interface{}{
								"cpu":    "{{.CPU}}",
								"memory": "{{.Memory}}",
							},
							"limits": map[string]interface{}{
								"cpu":    "{{.CPULimit}}",
								"memory": "{{.MemoryLimit}}",
							},
						},
						"volumeMounts": []map[string]interface{}{
							{
								"name":      "training-data",
								"mountPath": "/data",
							},
							{
								"name":      "training-code",
								"mountPath": "/workspace",
							},
							{
								"name":      "training-output",
								"mountPath": "/output",
							},
						},
					},
				},
				"volumes": []map[string]interface{}{
					{
						"name": "training-data",
						"persistentVolumeClaim": map[string]interface{}{
							"claimName": fmt.Sprintf("training-%s-data-pvc", job.JobID),
						},
					},
					{
						"name": "training-code",
						"persistentVolumeClaim": map[string]interface{}{
							"claimName": fmt.Sprintf("training-%s-code-pvc", job.JobID),
						},
					},
					{
						"name": "training-output",
						"persistentVolumeClaim": map[string]interface{}{
							"claimName": fmt.Sprintf("training-%s-output-pvc", job.JobID),
						},
					},
				},
			},
			Status: map[string]interface{}{
				"phase": "Pending",
			},
			CreatedAt: time.Now(),
			UpdatedAt: time.Now(),
		},
		{
			Type:      "PersistentVolumeClaim",
			Name:      fmt.Sprintf("training-%s-data-pvc", job.JobID),
			Namespace: "training",
			Spec: map[string]interface{}{
				"accessModes": []string{"ReadWriteOnce"},
				"resources": map[string]interface{}{
					"requests": map[string]interface{}{
						"storage": "50Gi",
					},
				},
			},
			Status: map[string]interface{}{
				"phase": "Pending",
			},
			CreatedAt: time.Now(),
			UpdatedAt: time.Now(),
		},
		{
			Type:      "PersistentVolumeClaim",
			Name:      fmt.Sprintf("training-%s-code-pvc", job.JobID),
			Namespace: "training",
			Spec: map[string]interface{}{
				"accessModes": []string{"ReadWriteOnce"},
				"resources": map[string]interface{}{
					"requests": map[string]interface{}{
						"storage": "10Gi",
					},
				},
			},
			Status: map[string]interface{}{
				"phase": "Pending",
			},
			CreatedAt: time.Now(),
			UpdatedAt: time.Now(),
		},
		{
			Type:      "PersistentVolumeClaim",
			Name:      fmt.Sprintf("training-%s-output-pvc", job.JobID),
			Namespace: "training",
			Spec: map[string]interface{}{
				"accessModes": []string{"ReadWriteOnce"},
				"resources": map[string]interface{}{
					"requests": map[string]interface{}{
						"storage": "20Gi",
					},
				},
			},
			Status: map[string]interface{}{
				"phase": "Pending",
			},
			CreatedAt: time.Now(),
			UpdatedAt: time.Now(),
		},
	}

	return resources, nil
}

// GetResourceTemplates gets resource templates for a training job
func (h *TrainingJobTypeHandler) GetResourceTemplates(job *mysql.Job) ([]*job_manager.ResourceTemplate, error) {
	templates := []*job_manager.ResourceTemplate{
		{
			Type:        "Pod",
			Name:        "training-pod-template",
			Namespace:   "training",
			Description: "Machine learning training pod template",
			Template: map[string]interface{}{
				"apiVersion": "v1",
				"kind":       "Pod",
				"metadata": map[string]interface{}{
					"labels": map[string]interface{}{
						"app":      "ml-training",
						"job-id":   "{{.JobID}}",
						"job-type": "training",
					},
				},
				"spec": map[string]interface{}{
					"restartPolicy": "Never",
					"containers": []map[string]interface{}{
						{
							"name":  "training",
							"image": "{{.FrameworkImage}}",
							"command": []string{
								"python",
								"{{.ScriptPath}}",
							},
							"args": []string{
								"--data-path", "{{.DataPath}}",
								"--model-path", "{{.ModelPath}}",
								"--epochs", "{{.Epochs}}",
								"--batch-size", "{{.BatchSize}}",
								"--learning-rate", "{{.LearningRate}}",
							},
							"resources": map[string]interface{}{
								"requests": map[string]interface{}{
									"cpu":    "{{.CPU}}",
									"memory": "{{.Memory}}",
								},
								"limits": map[string]interface{}{
									"cpu":    "{{.CPULimit}}",
									"memory": "{{.MemoryLimit}}",
								},
							},
						},
					},
				},
			},
			Parameters: h.supportedParameters,
		},
	}

	return templates, nil
}

// StartJob starts a training job
func (h *TrainingJobTypeHandler) StartJob(ctx context.Context, job *mysql.Job) error {
	// Training-specific startup logic
	// This could include:
	// - Setting up the training environment
	// - Preparing data and code
	// - Configuring GPU resources
	// - Setting up monitoring

	return nil
}

// StopJob stops a training job
func (h *TrainingJobTypeHandler) StopJob(ctx context.Context, job *mysql.Job) error {
	// Training-specific shutdown logic
	// This could include:
	// - Saving model checkpoints
	// - Graceful shutdown of training process
	// - Cleanup of temporary files
	// - Saving training logs

	return nil
}

// GetJobStatus gets the status of a training job
func (h *TrainingJobTypeHandler) GetJobStatus(ctx context.Context, job *mysql.Job) (*mysql.JobStatus, error) {
	// Training-specific status logic
	// This could include:
	// - Checking training progress
	// - Getting loss/metrics
	// - Checking resource usage
	// - Monitoring GPU utilization

	status := &mysql.JobStatus{
		JobID:     job.JobID,
		Status:    string(job_manager.JobStateRunning),
		Message:   "Training job is running",
		CreatedAt: time.Now(),
	}

	return status, nil
}
